Spectral Wave Modeling in Very Shallow Water at Southern Coast of Caspian Sea

This study evaluates the capability of the Simulating WAves Nearshore (SWAN) wave model (version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones. However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth (K_pd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

Multiobjective optimisation of bogie suspension to boost speed on curves

To improve safety and maximum admissible speed on different operational scenarios, multiobjective optimisation of bogie suspension components of a one-car railway vehicle model is considered. The vehicle model has 50 degrees of freedom and is developed in multibody dynamics software SIMPACK. Track shift force, running stability, and risk of derailment are selected as safety objective functions. The improved maximum admissible speeds of the vehicle on curves are determined based on the track plane accelerations up to 1.5?m/s². To attenuate the number of design parameters for optimisation and improve the computational efficiency, a global sensitivity analysis is accomplished using the multiplicative dimensional reduction method (M-DRM). A multistep optimisation routine based on genetic algorithm (GA) and MATLAB/SIMPACK co-simulation is executed at three levels. The bogie conventional secondary and primary suspension components are chosen as the design parameters in the first two steps, respectively. In the last step semi-active suspension is in focus. The input electrical current to magnetorheological yaw dampers is optimised to guarantee an appropriate safety level. Semi-active controllers are also applied and the respective effects on bogie dynamics are explored. The safety Pareto optimised results are compared with those associated with in-service values. The global sensitivity analysis and multistep approach significantly reduced the number of design parameters and improved the computational efficiency of the optimisation. Furthermore, using the optimised values of design parameters give the possibility to run the vehicle up to 13% faster on curves while a satisfactory safety level is guaranteed. The results obtained can be used in Pareto optimisation and active bogie suspension design problems.     

Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle’s dynamics and improve stability and curving performance. A half-car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H_∞ control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball-screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second-order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one-car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.     

A framework for short-term traffic flow forecasting using the combination of wavelet transformation and artificial neural networks

The main objective of this paper is to develop a framework for short-term traffic flow forecasting models with high accuracy. Due to flow oscillations, the real-time information presented to the drivers through variable message signs, etc., may not be valid by the time the driver reaches the location. On the other hand, not all compartments of the flow signal are of same importance in determining its future state. A model is developed to predict the value of traffic flow in near future (next 5–35?minutes) based on the combination of wavelet transformation and artificial neural networks. This model is called the hybrid WT-ANN. Wavelet transformation is set to denoise the flow signal, i.e., filtering the unimportant fluctuations of the flow signal. Unimportant fluctuations are those that have little or no effect on the future condition of the signal. The neural network is set and trained to use previous data for predicting future flow. To implement the system, traffic data of US-101 were used from Next Generation Simulation (NGSIM). Results show that removing the noises has improved the accuracy of the prediction to a great extent. The model was used to predict the flow in three different locations on the same highway and a different highway in a different country. The model rendered highly reliable predictions. The proposed model predicts the flow of next 5?min on the same location with 2.5% Mean Absolute Percentage Error (MAPE) and of next 35?min with less than 12% MAPE. It predicts the flow on downstream locations for next 5?min with less than 8% MAPE and for the different highway with 2.3% MAPE.     

Optimization of integrated scheduling of handling and storage operations at automated container terminals

Increasing demand for containerization compels container terminals to improve their performance. Uncoordinated scheduling of operations is one of the main factors accounting for poor performance at automated container terminals (ACTs). To increase land utilization efficiency and lower operational times, a new storage system called the split-platform automated storage/retrieval system (SP-AS/RS) has been introduced for temporary storage of containers. This paper describes a multi-objective mixed-integer programming (MIP) model that is based on a combination of multiple interacting sub-tasks. It is aimed at optimizing the integrated scheduling of handling and storage operations in ACTs. The MIP model objective function is to minimize delays in the loading/unloading tasks of the cranes and the travel time of vehicles and platforms in the SP-AS/RS. At the same time, a simulated annealing algorithm (SAA) that provides near-optimal solutions for the problem in a reasonable computation time is appraised. The results of this study show that the objective function of the MIP model is, on average, 58 % lower than that of the non-integrated scheduling method. On the other hand, the best objective function values obtained by the SAA indicate only a 3.7 % disadvantage in comparison with optimal values determined by the MIP model, demonstrating that the SAA is able to provide near-optimal solutions for the integrated scheduling of handling and storage operations.     

Improving the efficiency of weigh in motion systems through optimized allocating truck checking oriented procedure

In the present paper, an effective procedure is proposed to determine the best location(s) for installing Weigh in Motion systems (WIM). The main objective is to determine locations for best performance, defined as the maximum number of once-checked trucks' axle loads and minimizing unnecessary actions. The aforesaid method consists of two main stages, including solving shortest path algorithm and selecting the best location for installing WIM(s). A proper mathematical model has also been developed to achieve objective function. The number of once-checked trucks, unnecessary actions and average installing costs are defined as criteria measures. The proposed procedure was applied in a road network using experimental data, while the results were compared with the usual methods of locating enforcement facilities. Finally, it is concluded that the proposed procedure seems to be more efficient than the traditional methods and local experts' points of view.     

Network performance improvement under stochastic events with long-term effects

This paper is devoted to the problem of improving network performance to withstand incidents such as earthquakes, which have long-term adverse-effect upon the networks. A measure of link importance is presented based on consumers surplus. This measure is then used to define and solve a network improvement problem. Computational complexities are reduced by introducing an approximate measure of link importance which performs almost as good, and hence may be used for solving large scale problems. Several example networks are used to clarify the discussion numerically.     

Robustness analysis of bogie suspension components Pareto optimised values

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.     

里海南部海岸在极浅水中的波谱建模研究（英文）

This study evaluates the capability of the Simulating WAves Nearshore(SWAN) wave model(version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones.However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth(Kpd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

Thermodynamic analysis of alternative marine fuels for marine gas turbine power plants

The marine shipping industry faces challenges to reduce engine exhaust emissions and greenhouse gases (GHGs) from ships, and in particular, carbon dioxide. International regulatory bodies such as the International Maritime Organization and National Environmental Agencies of many countries have issued rules and regulations to drastically reduce GHG and emissions emanating from marine sources. This study investigates the possibility of using natural gas and hydrogen as alternative fuels to diesel oil for marine gas turbines and uses a mathematical model to assess the effect of these alternative fuels on gas turbine thermodynamic performance. Results show that since natural gas is categorized as a hydrocarbon fuel, the thermodynamic performance of the gas turbine cycle using natural gas was close to that of the diesel case. However, the gas turbine thermal efficiency was found to be slightly lower for natural gas and hydrogen fuels compared to diesel fuel.